What is the difference between feature extraction with or without data augmentation?
There are two ways that you could perform data augmentation:

Up front, by expanding the input dataset into a larger one, performing a range of changes to each input then storing the result. This appears to be what you are suggesting.

Just in time, by sampling from possible augmentations on each epoch, or even per sample when building a mini-batch. This appears to be what Chollet is suggesting.


Chollet's approach allows for augmentation to include finer degrees of augmentation that are different each time an input is considered, e.g. rotations of any angle, selecting a slightly different area from a larger image each time. For your approach you could consider the same set of augmentations but they would have to be &quot;frozen in&quot; at the time of building a dataset, and you would not be able to consider all possible variations for each image because it would make the dataset too large.
Both approaches are valid, and both would be called data augmentation. Chollet's approach obtains better re-use of image samples in the long term, and may result in better generalisation in the final trained network. Your approach may allow for more efficient use of CPU time to obtain a result that passes a threshold in accuracy. In some cases the difference between approaches may be minor compared to that caused by other changes in hyperparameters.